利用Web of Science数据库提供的数据以及基于固定引文窗口下的篇均被引频次、论文作者数两项计量指标分析了图书情报学科论文作者数与论文影响力之间的关系。研究结果发现,从引文影响力角度看,图情论文的最佳合著规模为4人。在一些与...利用Web of Science数据库提供的数据以及基于固定引文窗口下的篇均被引频次、论文作者数两项计量指标分析了图书情报学科论文作者数与论文影响力之间的关系。研究结果发现,从引文影响力角度看,图情论文的最佳合著规模为4人。在一些与科学计量学有关的情报学期刊上,独著论文的影响力不仅要高于某些形式的合著论文,而且当合著人数超过两人时,还出现了篇均被引频次随合著人数增加而下降的现象。对此现象,从论文选题的角度进行了部分讨论。展开更多
Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. T...Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.展开更多
文摘利用Web of Science数据库提供的数据以及基于固定引文窗口下的篇均被引频次、论文作者数两项计量指标分析了图书情报学科论文作者数与论文影响力之间的关系。研究结果发现,从引文影响力角度看,图情论文的最佳合著规模为4人。在一些与科学计量学有关的情报学期刊上,独著论文的影响力不仅要高于某些形式的合著论文,而且当合著人数超过两人时,还出现了篇均被引频次随合著人数增加而下降的现象。对此现象,从论文选题的角度进行了部分讨论。
文摘Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.
